Using feature learning for a medical text classification problem
Having just looked through a few entries from the corpus, I'd personally be skeptical of the applicability of any naive approaches.


In particular light of your small training set, I'd recommend that
whatever method you use should be able to produce human-readable
explanations for the operation of the classifier it builds (e.g.
decision trees/learning classifier systems/genetic programming):
this allows 'common sense' tuning of the classifier, rather than the
danger of overfitting to the training set via black box parameter
optimization.
Rather than throw the entire 'bag of words' at a classifier and hope
that the appropriate set of features will be extracted, you should
first consider what kind of criteria you as a human being might use
to make that decision, and how you might be able to pre-process to
produce features (e.g. syllable-length, metrics from ConceptNet etc) that
are as close to these as reasonably possible.
Having used some human intuition to obtain a reasonable set of
feature primitives, then you can build your classifier and obtain
higher-level expressions that discriminate between them.